From 2D Drug Screening to Predictive Pharmacology: Redefining Preclinical Models in Oncology

Cancer drug development still relies heavily on preclinical models that often fail to predict clinical efficacy. Although two-dimensional (2D) cell cultures and animal models have contributed significantly to cancer research, they do not adequately capture the complexity, heterogeneity, and microenvironmental conditions of human tumors. As a result, pharmacological findings generated with these systems frequently show limited clinical translation. This review discusses the conceptual distinction between drug activity and predictive pharmacology, arguing that successful target modulation in simplified experimental systems does not necessarily predict therapeutic benefit in patients. The limitations of conventional preclinical approaches, including homogeneous drug exposure in 2D cultures and species-specific differences in animal models, are briefly examined. This review further highlights the potential of human-relevant models, such as patient-derived organoids and microphysiological systems, to improve the predictive value of preclinical testing. These platforms allow more realistic evaluation of drug response, resistance mechanisms, and functional biomarkers under conditions that better resemble human tumor biology. Altogether, the integration of functionally informative models into drug development pipelines may support more accurate and clinically relevant pharmacological decision-making.